distill-test-set

Name: distill-test-set
Availability: InStock
Author: Coval

Distill large datasets into compact, failure-weighted test suites for AI evaluation.

2|Updated Feb 17, 2026

Search Tags:#automation #testing #evaluation #deduplication #dataset #coval

Ready to run

npx skills add https://github.com/coval-ai/coval-external-skills --skill distill-test-set

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: distill-test-set
Source: https://github.com/coval-ai/coval-external-skills/tree/main/skills/test-cases/distill-test-set
Command: npx skills add https://github.com/coval-ai/coval-external-skills --skill distill-test-set

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill solves the problem of bloated, low-quality test sets that are expensive to run and provide poor signal, by automatically deduplicating and selecting a representative, failure-weighted subset of test cases.

Core Features & Use Cases

Intelligent Deduplication: Removes exact duplicates and paraphrased scenarios while preserving distinct intents.
Failure-Weighted Selection: Prioritizes known agent failures to ensure the most critical scenarios are included in your evaluation.
Boilerplate Management: Automatically caps generic fallback or greeting responses to prevent them from flooding your test suite.
Use Case: Use this when you have thousands of raw conversation logs or an oversized test set and need to distill them into a high-impact 20-50 case suite for efficient agent evaluation.

Quick Start

Run the distill test set skill by providing the source dataset identifier to the command line interface to generate a refined test set file.

Frequently Asked Questions about distill-test-set

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I distill a noisy dataset into a high-signal test suite?▼

You distill a noisy dataset by running the skill with your source dataset identifier, which applies deduplication, paraphrase clustering, and failure-weighted sampling to generate a compact, high-signal test suite.

What is failure-weighted sampling for AI evaluation?▼

Failure-weighted sampling for AI evaluation is a selection method that prioritizes known agent failures, ensuring the most critical scenarios are included when you distill large datasets into a refined test suite.

How do I deduplicate conversation logs and paraphrased scenarios for testing?▼

You deduplicate conversation logs by removing exact duplicates and clustering paraphrased scenarios while preserving distinct intents, utilizing Python-based heuristics or OpenAI embeddings to ensure representative coverage.

Can I reduce thousands of raw conversation logs into a 50-case test suite?▼

Yes, you can reduce thousands of raw conversation logs into a compact 20-50 case test suite by automatically capping generic fallback responses and selecting a representative, failure-weighted subset for efficient agent evaluation.

Do I need OpenAI embeddings to deduplicate test sets across multiple languages?▼

No, you do not need OpenAI embeddings to deduplicate test sets; the skill can alternatively utilize Python-based heuristics to ensure representative coverage across intent labels and languages.